Clustering and classification of music by interval categories

Aline Honingh*, Rens Bod

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

We present a novel approach to clustering and classification of music, based on the concept of interval categories. Six interval categories exist, each with its own musical character. A piece of music can be represented by six numbers, reflecting the percentages of occurrences of each interval category. A piece of music can, in this way, be visualized as a point in a six dimensional space. The three most significant dimensions are chosen from these six. Using this approach, a successful visual clustering of music is possible for 1) composers through various musical time periods, and 2) the three periods of Beethoven, which illustrates the use of our approach on both a general and a specific level. Furthermore, we will see that automatic classification between tonal and atonal music can be achieved.

Original languageEnglish
Title of host publicationMathematics and Computation in Music - Third International Conference, MCM 2011, Proceedings
Pages346-349
Number of pages4
DOIs
Publication statusPublished - 30 Jun 2011
Externally publishedYes
Event3rd International Conference on Mathematics and Computation in Music, MCM 2011 - Paris, France
Duration: 15 Jun 201117 Jun 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6726 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Mathematics and Computation in Music, MCM 2011
CountryFrance
CityParis
Period15/06/1117/06/11

Keywords

  • Classification
  • Clustering
  • Interval Category
  • Pitch-class Set

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    Honingh, A., & Bod, R. (2011). Clustering and classification of music by interval categories. In Mathematics and Computation in Music - Third International Conference, MCM 2011, Proceedings (pp. 346-349). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6726 LNAI). https://doi.org/10.1007/978-3-642-21590-2_30